Considering learning styles and context-awareness for mobile adaptive learning

2015 ◽  
Vol 22 (1) ◽  
pp. 297-315 ◽  
Author(s):  
Richard A. W. Tortorella ◽  
Sabine Graf
Keyword(s):  
Learning Styles ◽  
Adaptive Learning ◽  
Context Awareness

scholarly journals Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020 ◽  
Vol 10 (2) ◽  
pp. 42
Author(s):  
Othmar Othmar Mwambe ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Learning Styles ◽  
Real Time ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Accurate Information ◽  
Interactive Learning Environment ◽  
Time Dynamic ◽  
E Learning ◽  
Time Basis ◽  
User Friendly

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

scholarly journals Adaptive Learning Spaces with Context-Awareness

2019 ◽  
pp. 7-13
Author(s):  
Valéry Psyché ◽  
Ben Daniel ◽  
Jacqueline Bourdeau
Keyword(s):  
Adaptive Learning ◽  
Context Awareness ◽  
Learning Spaces

The Learning Style-Based Adaptive Learning System Architecture

2015 ◽  
Vol 5 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Chyun-Chyi Chen ◽  
Po-Sheng Chiu ◽  
Yueh-Min Huang
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
System Architecture ◽  
Learning System ◽  
Learning Materials ◽  
Learning Path ◽  
Different Types ◽  
Learning System Architecture ◽  
Adaptive Learning System

In the current study of learning process that show learners will take a different way and use different types of learning resources in order to learning better. Any many researchers also agree that learning materials must be able to meet the various learning styles of learners. Therefore, let learners can effective to improve their learning, for different learning styles of learners should be given different types of learning materials. In this paper the authors propose a learner's learning style-based adaptive learning system architecture that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. In this adaptive learning system architecture, it will according to different learning styles given different types of learning materials and will according to learner's profile to adjust learner's learning style for providing suitable learning materials.

scholarly journals SCIENCE EDUCATION ADAPTIVE LEARNING SYSTEM AS A COMPUTER-BASED SCIENCE LEARNING WITH LEARNING STYLE VARIATIONS

2018 ◽  
Vol 17 (4) ◽  
pp. 711-727 ◽  
Author(s):  
Zulfiani Zulfiani ◽  
Iwan Permana Suwarna ◽  
Sujiyo Miranto
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Science Learning ◽  
Learning System ◽  
Learning Material ◽  
Learning Approaches ◽  
Slow Learners ◽  
Computer Based ◽  
Adaptive Learning System

Students with their different learning styles also have their own different learning approaches, and teachers cannot simultaneously facilitate them all. Teachers’ limitation in serving all students’ learning styles can be anticipated by the use of computer-based instructions. This research aims to develop ScEd-Adaptive Learning System (ScEd-ASL) as a computer-based science learning media by accommodating students’ learning style variations. The research method used is a mixed method at junior high schools in Tangerang Selatan. The final product of the research is a special learning media appropriate to students’ visual, aural, read/write and kinesthetic learning styles. The uniqueness of the media is its form of integrated science materials, accommodating fast and slow learners, and appropriate to their learning styles. ScEd-Adaptive Learning System as a developed computer-based science learning media was declared as good and valid by four media experts and five learning material experts. ScEd-ALS for kinesthetic style has a high effectivity to improve students learning mastery (100%), consecutively aural (63%), read/write (55%), and visual (20%). This media development can be continued with the Android version or iOS to make it more operationally practical. Keywords: adaptive learning system, science learning media, computer-based instruction, learning style.

scholarly journals Applying adaptive learning by integrating semantic and machine learning in proposing student assessment model

2022 ◽  
Vol 12 (2) ◽  
pp. 2014
Author(s):  
Kamilia Hosny ◽  
Abeer El-korany
Keyword(s):  
Machine Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Student Assessment ◽  
Assessment Method ◽  
Assessment Model ◽  
Clustering Methods ◽  
Proposed Model ◽  
Data Driven Approach

<p>Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.</p>

Individualization of Foreign Language Teaching through Adaptive eLearning

2017 ◽  
Vol 15 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Katerina Kostolanyova ◽  
Stepanka Nedbalova
Keyword(s):  
Foreign Language ◽  
Learning Styles ◽  
Language Learning ◽  
Adaptive Learning ◽  
Informal Education ◽  
Current Knowledge ◽  
Language Teaching ◽  
Foreign Language Teaching ◽  
The Czech Republic ◽  
The University

Lifelong learning has become an essential part of each profession. For this reason, personalized and adaptive learning has been drawing attention of professionals in the field of formal as well as informal education in the last few years. The effort has been made to design adaptive study supports regarding students' requirements, abilities and current knowledge. In the Czech Republic, particularly at the University of Ostrava, a team of educators, didactics professionals and IT professionals has been applying their mind to personalized learning in the electronic environment. They have been developing a suitable learning environment to fit students' learning styles. The paper describes a general model and a theory of adaptive eLearning from the perspective of the University of Ostrava professionals. It also demonstrates hard facts of the research in the field of language learning. This paper, Individualization of foreign language teaching through adaptive eLearning, is an extended version of the paper published in the ICWL 2015 workshop proceedings.

Artificial Intelligence in Education

2019 ◽  
pp. 224-236 ◽  
Author(s):  
Nil Goksel ◽  
Aras Bozkurt
Keyword(s):  
Artificial Intelligence ◽  
Learning Styles ◽  
Language Processing ◽  
Adaptive Learning ◽  
Future Perspectives ◽  
Tutoring Systems ◽  
Educational Processes ◽  
Key Concepts ◽  
Artificial Intelligence In Education ◽  
Educational Perspective

Though only a dream a while ago, artificial intelligence (AI) has become a reality, being now part of our routines and penetrating every aspect of our lives, including education. It is still a field in its infancy, but as time progresses, we will witness how AI evolves and explore its untapped potential. Against this background, this chapter examines current insights and future perspectives of AI in various contexts, such as natural language processing (NLP), machine learning, and deep learning. For this purpose, social network analysis (SNA) is used as a guide for the interpretation of the key concepts in AI research from an educational perspective. The research identified three broad themes: (1) adaptive learning, personalization and learning styles, (2) expert systems and intelligent tutoring systems, and (3) AI as a future component of educational processes.

. Learning analytics technology in adaptive electronic learning environments.

2021 ◽  
Vol 92 (2) ◽  
pp. 144-153
Author(s):  
M.R. Attia ◽  
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Adaptive Learning ◽  
Learning Analytics ◽  
Rapid Identification ◽  
Electronic Learning ◽  
E Learning ◽  
Mental Abilities ◽  
The Difference ◽  
As If

Adaptive e-learning environments are based on diversifying the presentation of content according to the learning styles of each learner, where the content is presented as if it is directed to each student separately, and activities and tests are presented so that they are sensitive to the different styles of learners and suitable for their mental abilities. These environments depend in their design on intelligence, therefore, these environments can analyze the characteristics and capabilities of learners, each separately, and this is done through learning analytics technology that helps in the rapid identification of the patterns of learners and the development of their behavior within the environment. In this article, firstly we review what adaptive learning environments and its characteristics are; the difference between adaptable and adaptive environments; components of adaptive learning environments. Learning analytics technology is also highlighted; and its importance in adaptive e-learning environments.

Sign in / Sign up

Export Citation Format

Share Document